Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

8 papers of 6,984Sort Recent · Most cited
  1. 2024
    CAFTTA: Mitigating Unseen Class Forgetting in Test-Time Adaptation with Knowledge FusionByung-Joon Lee, Jin-Seop Lee, Jee-Hyong LeeJoint 13th International Conference on Soft Computing and… · Sungkyunkwan University
  2. 2022
    RCRL: Replay-based Continual Representation Learning in Multi-task Super-ResolutionJin-Yong Park, Minha Kim, Simon S. WooIEEE International Conference on Advanced Video and Signa… · Sungkyunkwan University
  3. 2022
    Structure Learning-Based Task Decomposition for Reinforcement Learning in Non-stationary EnvironmentsHonguk Woo, Gwangpyo Yoo, Minjong YooAAAI · Sungkyunkwan University
  4. 2021
    CoReD: Generalizing Fake Media Detection with Continual Representation using DistillationMinha Kim, Shahroz Tariq, Simon S. WooACM MM · Sungkyunkwan University
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  5. 2021
    SS-IL: Separated Softmax for Incremental LearningHongjoon Ahn, Jihwan Kwak, Subin Lim … Taesup MoonICCV · Sungkyunkwan University · Seoul National University
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  6. 2021
    SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental LearningSungmin Cha, Beomyoung Kim, Youngjoon Yoo, Taesup MoonNeurIPS · Sungkyunkwan University · Naver (South Korea) · +1
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  7. 2020
    Continual Learning with Node-Importance based Adaptive Group Sparse RegularizationSangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup MoonNeurIPS · Sungkyunkwan University
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  8. 2019
    Uncertainty-based Continual Learning with Adaptive RegularizationHongjoon Ahn, Sungmin Cha, Dong-Gyu Lee, Taesup MoonNeurIPS · Sungkyunkwan University
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.